In 2015, Gatys et al.  [1]} proposed a novel method to transfer an image (called content image) to a new image (called stylized image) with a style of another image (called style image). This method is based on the assumption that a pretrained deep neural network for classification on a large dataset can represent the content and style of any image. The basic idea is to fix the neural network and optimize the input such that its content representation and style representation by the neural network match those of the content image and the style image, respectively. However, optimization is an iterative and time-consuming process. Learning-based methods have been proposed to directly map the content image to the stylized image using a feedforward neural network  [2]}, [3]}, which are several orders of magnitude faster than the optimization-based method. A different image stylization method is presented in [4]} to stylize an image according to the style defined by a set of images. One limitation of these methods is that different models must be trained for different style images. Several recent methods [5]}, [6]}, [7]}, [8]}, [9]} have been shown to be able to learn multiple styles simultaneously.
